Received: 22 September 2018 Revised: 2 December 2018 Accepted: 8 January 2019 DOI: 10.1002/met.1778
RESEARCH ARTICLE
Investigation of drought in the northern Iraq region
Kasım Yenigun1 | Wlat A. Ibrahim2
1Civil Engineering Department, Harran University, Engineering Faculty, S¸anlıurfa, Turkey Drought is a phenomenon of climate and is one of the catastrophic events that 2Graduate School of Natural and Applied cause much damage on each occurrence. One of the ways of drought adjustment, Sciences, Harran University, S¸anlıurfa, Turkey evaluation and drought monitoring is based on indicators that can be used to deter- Correspondence mine its extent and continuity in a region. In this study, drought analysis (the dura- Kasim Yenigun, Civil Engineering Department, tion and severity of drought) in the north Iraq region was studied by using the Harran University, Engineering Faculty, Osmanbey Campus, 63200, S¸anlıurfa, Turkey. Standardized Precipitation Index (SPI) for time intervals of 1, 3, 6, 9 and Email: [email protected] 12 months. That feature of this method helps to compare the drought events in dif- ferent places and scales. To observe dry and wet periods and the severity and length of drought monthly rainfall data of 15 meteorological stations of the north- ern Iraq provinces from 1979 to 2013 were used. Calculations were performed on the SPI by using the SPI code in MATLAB computer software. The results of the study showed that the continuity of dry periods in the 6, 9 and 12 month periods was higher than in the 1 and 3 month time intervals. Moreover, according to the calculation, the driest year was observed in 2008. This analysis is essential because it gives full information about the longest dry and wet periods for all stations of the region.
KEYWORDS drought, drought duration, drought severity, northern Iraq, standard precipitation index
1 | INTRODUCTION length of the drought, and they are defined as meteorologi- cal, agricultural, hydrological and socioeconomic drought. Natural disasters such as floods, thunderstorms, drought are It should also be noted that the effects of rainfall reduc- characteristics of our environment. These disasters make a tion on soil moisture, water reservoirs, surface currents of negative impact and irreparably damage human life. Accord- rivers and groundwater levels are shown at different time ing to the literature, the number and incidence of these scales (Lloyd-Hughes and Saunders, 2002). events have increased over the past 30 years. About 25% of Drought definitions, especially regarding the impact on them are events related to climatic factors. Drought is one of the natural and social environment, are always changing. It the main causes of these events, perhaps the most influential seems reasonable to relate drought to a large scale with time, (Kogan, 1990). period length and location of its occurrence (Tsakiris and Drought is one of the significant hazards associated with Vangelis, 2003). meteorology. This natural hazard affects all aspects of our In advanced stages of drought, water resources encounter life. At the international level, there is no single definition of a severe shortage. In most areas of the world, groundwater drought that is universally accepted. In general, drought resources have been exploited as a source for public con- occurs when there is a decrease in water, both at the site and sumption as well as agricultural activities (Scheidleder et al., at a specific time (Correia et al., 1991). Each drought is 1999); this means that the reaction of groundwater to characterized by three characteristics: intensity, length and drought has become important (Calow et al., 1999). width. There are a variety of types of drought due to the Although underground is one of the most critical water
Meteorol Appl. 2019;1–10. wileyonlinelibrary.com/journal/met © 2019 Royal Meteorological Society 1 2 YENIGUN AND IBRAHIM sources in the world, it is not considered in many studies of monthly total rainfall data collected from 15 meteorological drought. observation stations. The SPI which is widely accepted In any case, for quantitative analysis of drought, the exis- throughout the world was used to determine the drought. tence of a specific indicator for the accurate determination of Temporal drought values were evaluated according to 1, 3, wet and dry periods is essential (Silva, 2003). 6, 9 and 12 month time scales for determining the dry and The efficiency of drought monitoring systems is pro- wet periods. foundly influenced by the accuracy of the selection of indi- The main difference between this and previous studies cators that provide a detailed description of the actual state for northern Iraq’s drought is the data used (Awchi and of drought conditions. In recent years, several indicators Kalyana, 2017). These are more accurate, because the have been proposed for the study of drought. Moreover, nec- amount of missing data is less than in previous work. Thus, essarily each of these indicators is related to one of four the result is more accurate. The longest dry and wet periods types of drought (Mendicino et al., 2008), e.g. the Palmer for all stations were found in this study. All situations for all Drought Severity Index (PDSI), the Shafer and Dezmann time scales are shown on the maps by using ArcMap soft- Surface Water Supply Index, the Ball and Mole Index, the ware. The percentages of all situations for all time scales Johnville and others index, and the Standardized Precipita- were found. Also the number of stations is more than in pre- tion Index (SPI) which was introduced by McKee and others vious studies. in 1993 (McKee et al., 1993). After a general evaluation of the literature, it can be seen that drought analysis is critical for any region to understand 3 | MATERIALS AND METHODS its daily condition and to design some water structures or plan water management. Beside this, there is no detailed 3.1 | Study area study on drought in the northern Iraq region, an essential The study area of northern Iraq has an area of 40,643 km2. part of Iraq which has important rivers. This situation encouraged the preparation of this study. North Iraq is at 36 N and 44 E. The southern parts of Iraq are located in the tropics and its northern parts are in semi- arid regions. Figure 1 shows the study area. There are four 2 | LITERATURE STUDY main rivers in northern Iraq, the Euphrates, Tigris, Diyala and the Lower Zab. Palmer (1965) proposed the PDSI which is based on produc- The Euphrates is one of the two major rivers in Asia and tion and demand for water balance. After that, some the Middle East. It is the longest river in the region with a researchers realized that an index is easy to calculate and sta- length of 1,700 miles (2,800 km). The Tigris is the second tistically relevant and meaningful. Moreover, the under- largest river in Iraq with a length of 1,150 miles (1,850 km). standing that a deficit of precipitation has different impacts They come from the eastern mountains of Turkey. The on groundwater, reservoir storage, soil moisture, snowpack Tigris river arrives in Iraq, where it connects with the and streamflow led scientists like McKee to develop the SPI Euphrates to form the Shat Al-Arab River. in 1993. This was used for western Kansas, central Iowa and The Diyala river is the third highest river in Iraq at Colorado, United States (Shafer and Dezman, 1982; McKee 277 miles (445 km). This river comes from the Zagros et al., 1993), and in Hungary (Szalai et al., 2000). Mountains in western Iran and flows to Iraq through the Drought severity characteristics using the SPI were stud- lowlands. The Lower Zab, also known as the Little Zab, ied by Hayes et al. (1999), in Catalonia, Spain, by Lana comes from the Zagros Mountains in Iran, where it is tied to et al. (2001), in China (Wu and Hayes, 2001), in Cordoba, the Tigris. The river is 249 miles (400 km) long. It passes Argentina (Lloyd-Hughes and Saunders, 2002), in Greece through different environmental zones that result in the (Loukas et al., 2004), in the Aegean region, Greece (Pamuk growth of different plants (Sawe, 2017). et al., 2004), in Trakya, Turkey (Çaldag et al., 2004), in S¸anlıurfa, Turkey (Tonkaz et al., 2005), in the Awash River 3.2 | Data basin, Ethiopia (Edossa et al., 2010), in Albania (Merkoci et al., 2013), in Iraq (Al-Timimi et al., 2013), in Antakya- Meteorological data are used in this study. Rainfall is the Kahramanmaras¸, Turkey (Karabulut, 2015), in S¸anlıurfa, main parameter of the evolution of a meteorological drought, Turkey (Gümüs and Basak, 2017), in the Seyhan–Ceyhan so rainfall data were collected from the meteorological river basins, Turkey (Gümüs¸ and Algın, 2017), and in north- observation stations in different cities of northern Iraq ern Iraq (Awchi and Kalyana, 2017). (Meteorological Organization of North Iraq and Global The aim of this study was to find the longest dry and wet Weather Data) and the software needed was available in the periods and their severity for different meteorological sta- laboratory of the Civil Engineering Department of Harran tions in the northern Iraq region. A drought analysis of the University. Table 1 gives the general information of the sta- area under consideration was carried out by using long-term tions used in the drought analysis. According to this, YENIGUN AND IBRAHIM 3
FIGURE 1 Study area [Colour figure can be viewed at wileyonlinelibrary.com]
TABLE 1 The climatic and geographical characteristics of all stations
Station no. Station ID Station name Longitude Latitude Height (m) Available years range 1 361441 Erbil 44.009167 36.191111 439 1979–2013 2 358434 Makhmur 43.58102 35.774954 252 1979–2013 3 364441 Pirmam 44.196151 36.385931 507 1979–2013 4 354453 Sulaymaniyah 45.375611 35.564112 886 1979–2013 5 351456 Darbandikhan 45.695272 35.110939 532 1979–2013 6 354447 Chamchamal 44.821728 35.529211 859 1979–2013 7 351453 Sangaw 45.179867 35.285127 770 1979–2013 8 361450 Dokan 44.962103 35.949559 529 1979–2013 9 370428 Duhok 42.948857 36.867905 795 1979–2013 10 364419 Sinjar 41.865044 36.322072 1,067 1979–2013 11 354444 Kirkuk 44.316667 35.466667 319 1979–2013 12 345453 Khanaqin 45.383938 34.354254 188 1979–2013 13 348447 Tuz Khurma 44.620763 34.880964 197 1979–2013 14 364431 Mosul 43.164 36.356648 233 1979–2013 15 364425 Tal Afar 42.403672 36.362358 485 1979–2013
Sulaymaniyah (354453) had the highest rainfall amount of Dokan, Sinjar, Tal Afar, Mosul and Khanaqin during a the studied stations, and Makhmur (358434) had the least 34 year statistical period (from 1979 to 2013) from the amount of rainfall. Average annual precipitation values Meteorological Organization of North Iraq and Global range from 765 to 241 mm at all stations. Weather Data was obtained. In this research, the annual rainfall of 15 synoptic sta- After initial data analysis, incomplete data in the tions at Erbil, Sulaymaniyah, Duhok, Darbandikhan, Cham- monthly series were constructed by the ratio method using chamal, Sangaw, Kirkuk, TuzKhurma, Makhmur, Pirmam, the statistics of the adjacent station. To reconstruct some of 4 YENIGUN AND IBRAHIM the statistical deficiencies in the Erbil, Sulaymaniyah and TABLE 2 The SPI classification Duhok stations, the synoptic stations of Chamchamal and SPI classes Period classification Sangaw were used, but in some stations no long-term statisti- SPI ≤−2 Extreme drought cal period could cover long distances; these years were elimi- −2 < SPI ≤−1.5 Severe drought nated from the calculations. Monthly rainfall data in northern −1.5 < SPI ≤−1 Moderate drought Iraq stations were collected from 1979 to 2013. Fifteen sta- –1 < SPI ≤ 0 Mild drought tions were selected. They were obtained from the Meteorolog- 0 < SPI ≤ 1 Mild wet ical Organization of North Iraq and Global Weather Data. 1 < SPI ≤ 1.5 Moderate wet Some of the climatic and geographical characteristics of the 1.5 < SPI ≤ 2 Wet stations are presented in Table 1. The location of the meteoro- SPI > 2 Extreme wet logical precipitation stations is given in Figure 2. extreme wet to extreme drought as shown in Table 2. The 3.3 | Methods general statistics of the data are given in Table 3. 3.3.1 | Standardized Precipitation Index (SPI) method With this index, first the amount of monthly rainfall is The SPI is used for investigation of drought. One of the calculated at each station for each time scale (1, 3, 6, 9, main criteria for the evaluation of drought using the SPI is 12 months). For example, at the 3 month scale each of the that the calculation requires the average and long-term stan- rainfalls in April, May and June make up the precipitation dard deviation of rainfall data during the period of study index in June; the total rainfall of May, June and July is an (Bonaccorso et al., 2003). This measure is provided primar- indicator of July. At the 6 month time scale the total rainfall ily to define and monitor drought and rain (Tsakiris et al., of the last month and 5 months before will be an index of 2003) and allows the analysis to define and identify the rainfall per month. Similar to other measures, cumulative number of dry and wet periods for any described time rainfall time series are calculated for each month. Thus, for (McKee et al., 1993). Since the index is dimensionless, that example, for the 6 month time scale there is not a specific information can help to compare different areas. Moreover, period of 6 months per year (first or second 6 months), but produce drought range with higher accuracy (Agnew, 2000) all the intervals of 6 months in the period are included. one of the other advantages of this indicator, its ability to Then, the cumulative rainfall per month is fitted with a detect extreme droughts and extreme wet in the region, to do gamma probability distribution. After calculating the cumu- a lot of analysis on it (Livada and Assimakopoulos, 2006). lative probability scale at any time scale and for each month Details of the SPI method can be found in Gumus and of the year this possibility becomes a normal random vari- Algin (2017). able, i.e. is the same standard Z. The SPI values have been classified by Edossa et al. The SPI, due to the simplicity of calculation, has the (2010) into eight classes that vary within the range from ability to calculate for any period the most appropriate
FIGURE 2 The regional distribution of long-term average annual precipitation of all used stations [Colour figure can be viewed at wileyonlinelibrary.com] YENIGUN AND IBRAHIM 5
TABLE 3 The general statistics of the data for the stations
Station no. Station ID Station name Mean Min Max Annual precipitation (mm) 1 361441 Erbil 548.91 216.79 872.82 577 2 358434 Makhmur 260.16 109.80 523.57 298 3 364441 Pirmam 568.15 228.24 600.66 601 4 354453 Sulaymaniyah 778.18 244.65 1,304.30 765 5 351456 Darbandikhan 546.11 170.95 783.19 505 6 354447 Chamchamal 554.66 171.09 874.58 555 7 351453 Sangaw 669.67 205.07 943.92 631 8 361450 Dokan 619.78 254.75 1,012.93 670 9 370428 Duhok 552.06 222.26 988.72 572 10 364419 Sinjar 456.29 162.47 727.81 452 11 354444 Kirkuk 321.67 105.64 568.87 347 12 345453 Khanaqin 228.03 97.67 370.82 241 13 348447 TuzKhurma 337.15 134.00 583.75 354 14 364431 Mosul 535.57 199.73 1,045.88 598 15 364425 Tal Afar 442.68 153.35 741.87 450
indicator for monitoring the meteorological drought. 8 rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Positive values of the SPI index indicate that rainfall is more > 1 <> ln 0 3 SPI-6 2 1 0 SPI –1 –2 –3 –4 October 1979October 1981October 1983October 1985October 1987October 1989October 1991October 1993October 1995October 1997October 1999October 2001October 2003October 2005October 2007October 2009October 2011 Date (Month) FIGURE 3 The temporal distribution of SPI values for dry and wet situations in Kirkuk station according to the 1, 3, 6, 9 and 12 month SPI 4.1 | Temporal variation of drought According to the values of the 9 month SPI (SPI-9), The values of the temporal SPI according to the seasonal Makhmur has the highest percentage of dry months with periods (1) SPI-3 December, (2) SPI-3 March, (3) SPI-3 54%. The station with the highest percentage of extreme dry months was Tuz Khurma with 7.35%, Pirmam with 6.95% June, (4) SPI-3 September, (5) SPI-6 March, (6) SPI-6 for severe dry months; when the middle months are exam- September, (7) SPI-12 for 1979–2013 are given in Figure 5. ined, it is seen that the highest percentage of moderate dry The occurrence of drought and wet events according to SPI- months was in Duhok with 13.97%. Also, Makhmur is deter- 3 values are given in Figure S1. mined to be the station with the highest rate of mild arid months with 36.52%. 4.2 | Discussion on the drought situation of the When the 12 month values are examined, the percentage northern Iraq region of dry months in all months is highest at Makhmur with 56%. The highest percentage of extreme dry months in these 4.2.1 | Dry periods months is Sinjar and Tuz Khurma with 3.68%. Makhmur is Examining the dry periods, according to the SPI-1 values the observed as the highest severely dry station with 6.62%. The highest percentage of dry months in the long-term data is highest percentage of moderate dry months is 14.95% in found in Erbil, Pirmam, Duhok and Mosul with 40%, while Duhok, and the highest percentage of mild dry months is Kirkuk was the highest of extremely dry months with 40.44% in Makhmur. Dry months and percentages of the 2.86%. Erbil had the highest severe dry percentage with SPI-1, SPI-3, SPI-6, SPI-9 and SPI-12 values of all stations 4.52%, and the highest moderate dry percentage was Mosul considered are given in Tables S1–S5. with 6.9%. Duhok had the highest mild dry percentage with 29.7%. 4.2.2 | Wet periods According to the SPI-3 values, the highest percentage of The monthly highest wet percentages for the SPI -1 values dry months in the long-term data is found in Duhok with are 67% for Darbandikhan, Kirkuk and Khanaqin; in wet 50%. It is seen that Makhmur is the highest with 3.43% of months, the highest percentage of extreme wet months is extreme dry months. Darbandikhan and Dokan had the high- 2.38% in Erbil and Makhmur. The highest percentage of est severe dry percentage with 5.15%, Duhok a moderate dry severe wet months is 4.76% in Mosul. The highest percent- percentage of 9.31% and Erbil had the highest mild dry per- age (24.52%) of moderate wet months is observed in Tuz centage with 35.78%. Khurma. The maximum percentage of mild wet months is According to the SPI-6 values, the highest percentage of determined at Sulaymaniyah with 48.81%. dry months in the long-term data is found in Makhmur, Kha- For SPI-3 values the highest percentage of wet months is naqin and Mosul with 50%. It is seen that Darbandikhan is in Darbandikhan and Kirkuk with 55%. Erbil and Makhmur the highest of the extreme dry months with 4.17%. For the have the largest amount of extreme wet months with 2.21%. severe dry percentage, Erbil and Sinjar with 5.88% were For severe wet percentage, the highest is in Makhmur with highest, for moderate dry percentage Duhok with 12.25%, 5.15%. The highest percentage of moderate wet months is in and for the highest mild dry percentage Khanaqin Sulaymaniyah with 10.78%. The highest percentage of mild with 34.56%. wet months is determined to be Darbandikhan with 48.77%. YENIGUN AND IBRAHIM 7 SPI-1 SPI-3 31% 38% 69% 62% Wet Dry SPI-1 Dry Wet Dry SPI-1 Wet 2% SPI-3 Dry SPI-3 Wet 2% 9% 7% 6% 7% 7% 8% 15% 16% 34% 16% 57% 68% 70% 76% ED SD MOD MID MIW MOW SW EW ED SD MOD MID MIW MOW SW EW SPI-6 SPI-9 47% 53% 49% 51% Wet Dry SPI-6 Dry SPI-6 Wet 1% Wet Dry 7% 6% SPI-9 Dry 7% SPI-9 Wet 1% 5% 6% 23% 8% 19% 18% 28% 67% 70% 69% 65% ED SD MOD MID MIW MOW SW EW ED SD MOD MID MIW MOW SW EW SPI-12 SPI-12 Dry SPI-12 Wet 0% 5% 5% 7% 49% 23% 51% 40% 53% 67% Wet Dry ED SD MOD MID MIW MOW SW EW FIGURE 4 The distribution of SPI values for dry and wet situations according to the 1, 3, 6, 9 and 12 month SPI [Colour figure can be viewed at wileyonlinelibrary.com] TABLE 4 The longest dry and wet periods in Kirkuk station according to 1, 3, 6, 9 and 12 month SPI Dry period Wet period Start FinishTime Start Finish Time SPI-1 December 8–9 May 9–10 6 months December 88–April 91 November 89–March 92 12 months SPI-3 October 98–99 July 99–00 10 months February 86 May 87 16 months SPI-6 September 98 July 1 35 months October 81 October 84 37 months SPI-9 October 6 September 10 48 months October 79 December 88 112 months SPI-12 March 3 December 12 118 months October 79 March 89 115 months 8 YENIGUN AND IBRAHIM FIGURE 5 The values of temporal SPI according to the seasonal periods for 1979–2013: (1) SPI-3 December, (2) SPI-3 March, (3) SPI-3 June, (4) SPI-3 September, (5) SPI-6 March, (6) SPI-6 September, (7) SPI-12 [Colour figure can be viewed at wileyonlinelibrary.com] For the SPI-6 values and wet months, the percentage of of severe wet months is in Tuz Khurma with 6.13%. The wet months is about 55% for Tuz Khurma compared to all highest percentage of moderate wet months is in Tal Afar station data. The highest percentage of extreme wet months with 18.63%. Sulaymaniyah is determined to have the high- is 1.96% in Makhmur, and the highest percentage of severe est percentage of mild wet months with 38.24%. wet months is in Tuz Khurma with 5.39%. The highest per- For the SPI-12 values and wet months, the percentage of centage of moderate wet months is in Mosul with 15.69%. wet months is about 56% for Sangaw compared to all station Tuz Khurma is determined to have the highest percentage of data. The highest percentage of extreme wet months is mild wet months with 40.69%. 0.98% in Duhok, and the highest percentage of severe wet For the SPI -9 values and wet months, the percentage of months is in Makhmur with 6.37%. The highest percentage wet months is about 55% for Sangaw compared to all station of moderate wet months is in Tal Afar with 21.57%. Sulay- data. The highest percentage of extreme wet months is maniyah is determined to have the highest percentage of 0.98% in Makhmur and Pirmam, and the highest percentage mild wet months with 35%. Wet months and percentages of YENIGUN AND IBRAHIM 9 the SPI-1, SPI-3, SPI-6, SPI-9 and SPI-12 values of all sta- income levels is one of the indirect and second-order effects tions considered are given in Tables S6–S10. in these communities, the main reasons for which are the reduction of land use, the reduction of operations (irrigation) and harvesting. As a result of reduced food production, food 5 | CONCLUSIONS prices usually increase rapidly as a result of drought. Decreasing food production abnormally leads to rising food In this study, drought analysis of the northern Iraq area was prices, and lack of access to appropriate jobs reduces the carried out using long-term monthly total rainfall data mea- access of rural people to food; the occurrence of such prob- sured at 15 meteorological observation stations in the Erbil, lems, especially for small farmers and landless workers, is Sulaymaniyah, Duhok, Kirkuk and Mosul provinces. clear (Paul, 1998). So, in a time of drought, unpleasant and Due to the final results of this study, temporal drought unusual uses of existing water resources, along with the values for different time scales in the northern Iraq region weakness of water distribution systems, exacerbate the crisis were examined in detail. Based on the findings from the pre- (Karami and Keshavarz, 2010). sent drought analysis in the northern Iraq region by using monthly precipitation time series, the following conclusions may be drawn. ACKNOWLEDGEMENTS Considering the spatial and climatic characteristics of The authors thank the editors and the referees of the journal focal points in all three characteristics studied in a drought, for their valuable contribution and review. among the 50 stations Makhmur station has the highest fre- quency of drought occurrence on a 12 month time scale with 227 drought occurrences. Kirkuk station has the highest fre- Conflict of interest quency of wet occurrence on a 1 month time scale with The authors declare that they have no conflict of interest. 290 wet occurrences. Regarding the second feature studied, Erbil station has the longest duration of drought on all five time scales. Kirkuk and Chamchamal stations have the lon- ORCID gest duration of wet on all five time scales. The third charac- Kasım Yenigun https://orcid.org/0000-0002-3296-8687 teristic is drought severity. 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